



import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

SCREAMING_SNAKE_CASE__   			=   {
    '''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
    ''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'''
}


def A    (							__UpperCamelCase = "dhaka"			,						__UpperCamelCase = 5       )    ->   int:
			A__	  =     min(__UpperCamelCase			,						50       )  # Prevent abuse!
			A__	  =     {
			    'q': query,
			    'tbm': 'isch',
			    'hl': 'en',
			    'ijn': '0',
			}

			A__	  =     requests.get('https://www.google.com/search'			,						params=__UpperCamelCase			,						headers=__UpperCamelCase       )
			A__	  =     BeautifulSoup(html.text			,						'html.parser'       )
			A__	  =     ''.join(
			    re.findall(r'AF_initDataCallback\(([^<]+)\);'			,						str(soup.select('script'       )       )       )       )

			A__	  =     json.dumps(__UpperCamelCase       )
			A__	  =     json.loads(__UpperCamelCase       )

			A__	  =     re.findall(
			    r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'			,						__UpperCamelCase			,						)
			if not matched_google_image_data:
						return 0

			A__	  =     re.sub(
			    r'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]'			,						''			,						str(__UpperCamelCase       )			,						)

			A__	  =     re.findall(
			    r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]'			,						__UpperCamelCase			,						)
			for index, fixed_full_res_image in enumerate(__UpperCamelCase       ):
						if index >= max_images:
									return index
						A__	  =     bytes(__UpperCamelCase			,						'ascii'       ).decode(
						    'unicode-escape'       )
						A__	  =     bytes(__UpperCamelCase			,						'ascii'       ).decode(
						    'unicode-escape'       )
						A__	  =     urllib.request.build_opener()
						A__	  =     [
						    (
						        'User-Agent',
						        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
						        ' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582',
						    )
						]
						urllib.request.install_opener(__UpperCamelCase       )
						A__	  =     f'''query_{query.replace(" "			,						"_"       )}'''
						if not os.path.exists(__UpperCamelCase       ):
									os.makedirs(__UpperCamelCase       )
						urllib.request.urlretrieve(  # noqa: S310
						    __UpperCamelCase			,						f'''{path_name}/original_size_img_{index}.jpg'''       )
			return index


if __name__ == "__main__":
							try:
														SCREAMING_SNAKE_CASE__   			=   download_images_from_google_query(sys.argv[1])
														print(f'{image_count} images were downloaded to disk.')
							except IndexError:
														print('''Please provide a search term.''')
														raise












'''simple docstring'''







import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def       __UpperCamelCase					(      *lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       if not isinstance(lowercase_   ,       lowercase_   ):
              a_						     =			list(lowercase_   )
       for i in range(len(lowercase_   )   ):
              a_						     =			None
       gc.collect()
       if is_xpu_available():
              torch.xpu.empty_cache()
       elif is_npu_available():
              torch.npu.empty_cache()
       else:
              torch.cuda.empty_cache()
       return objects


def       __UpperCamelCase					(      lowercase_       :  Exception   ):
       """simple docstring"""




       a_						     =			[
           'CUDA out of memory.',  # CUDA OOM
           'cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.',  # CUDNN SNAFU
           'DefaultCPUAllocator: can\'t allocate memory',  # CPU OOM
       ]
       if isinstance(lowercase_   ,       lowercase_   ) and len(exception.args   ) == 1:
              return any(err in exception.args[0] for err in _statements   )
       return False


def       __UpperCamelCase					(      lowercase_       :  callable = None   ,       lowercase_       :  int = 128   ):
       """simple docstring"""




       if function is None:
              return functools.partial(lowercase_   ,       starting_batch_size=lowercase_   )

       a_						     =			starting_batch_size

       def decorator(*lowercase_       :  str   ,       **lowercase_       :  Dict   ):
              nonlocal batch_size
              gc.collect()
              if is_xpu_available():
                     torch.xpu.empty_cache()
              elif is_npu_available():
                     torch.npu.empty_cache()
              else:
                     torch.cuda.empty_cache()
              a_						     =			list(inspect.signature(lowercase_   ).parameters.keys()   )
              # Guard against user error
              if len(lowercase_   ) < (len(lowercase_   ) + 1):
                     a_						     =			', '.join([F'{arg}={value}' for arg, value in zip(params[1:]   ,       args[1:]   )]   )
                     raise TypeError(
                         F'Batch size was passed into `{function.__name__}` as the first argument when called.'
                         F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'   )
              while True:
                     if batch_size == 0:
                            raise RuntimeError('No executable batch size found, reached zero.'   )
                     try:
                            return function(lowercase_   ,       *lowercase_   ,       **lowercase_   )
                     except Exception as e:
                            if should_reduce_batch_size(lowercase_   ):
                                   gc.collect()
                                   if is_xpu_available():
                                          torch.xpu.empty_cache()
                                   elif is_npu_available():
                                          torch.npu.empty_cache()
                                   else:
                                          torch.cuda.empty_cache()
                                   batch_size //= 2
                            else:
                                   raise

       return decorator




